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Astrometric Alignment of Images

Project description

Build Status Coverage Documentation Status DOI Updates Python 3

ASTROALIGN is a simple package that will try to align two stellar astronomical images, especially when there is no WCS information available.

It does so by finding similar 3-point asterisms (triangles) in both images and deducing the affine transformation between them.

General registration routines try to match feature points, using corner detection routines to make the point correspondence. These generally fail for stellar astronomical images, since stars have very little stable structure and so, in general, indistinguishable from each other. Asterism matching is more robust, and closer to the human way of matching stellar images.

Astro align can match images of very different field of view, point-spread function, seeing and atmospheric conditions.

It may not work, or work with special care, on images of extended objects with few point-like sources or in very crowded fields.

You can find a Jupyter notebook example with the main features at


Using setuptools:

$ pip install astroalign

or from this distribution with

$ python install

Usage example

>>> import astroalign as aa
>>> aligned_image = aa.register(source_image, target_image)

In this example source_image will be interpolated by a transformation to coincide pixel to pixel with target_image and stored in aligned_image.

If we are only interested in knowing the transformation and the correspondence of control points in both images, use find_transform will return the transformation in a scikit-image SimilarityTransform object and a list of stars in source with the corresponding stars in target.

>>> transf, (s_list, t_list) = aa.find_transform(source, target)

source and target can each either be the numpy array of the image, or an iterable of (x, y) pairs of star positions on the image.

The returned transf object is a scikit-image SimilarityTranform object that contains the transformation matrix along with the scale, rotation and translation parameters.

s_list and t_list are numpy arrays of (x, y) point correspondence between source and target. transf applied to s_list will approximately render t_list.

This package is inspired by the program

TOROS Dev Team

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